An evaluation of semantic fisheye views for opportunistic search in an annotated image collection

Visual interfaces are potentially powerful tools for users to explore a representation of a collection and opportunistically discover information that will guide them toward relevant documents. Semantic fisheye views (SFEVs) are focus + context visualization techniques that manage visual complexity by selectively emphasizing and increasing the detail of information related to the user's focus and deemphasizing or filtering less important information. In this paper we describe a prototype for visualizing an annotated image collection and an experiment to compare the effectiveness of two distinctly different SFEVs for a complex opportunistic search task. The first SFEV calculates relevance based on keyword-content similarity and the second based on conceptual relationships between images derived using WordNet. The results of the experiment suggest that semantic-guided search is significantly more effective than similarity-guided search for discovering and using domain knowledge in a collectio

Concept expansion using semantic fisheye views

Exploratory search over a collection often requires users to iteratively apply a variety of strategies, such as searching for more general or more specific concepts in reaction to the information they encounter. Rich semantic models, such as WordNet, are potentially valuable aids for making sense of this information. However, these large complex models often contain specialized vocabularies and a detailed level of granularity that makes them difficult to use for opportunistic search. In this paper, we describe how Semantic Fisheye Views (SFEV) can be designed to transparently integrate rich semantic models into the search process, allowing users to effectively explore a diverse range of related concepts without explicitly navigating over the underlying model. The SFEV combines semantic guided search with interactive visualization techniques, creating a search tool that we have found to be significantly more effective for exploratory tasks than those based on keyword-similarity alone. © Springer-Verlag Berlin Heidelberg 2005

Theory and practice in visual interfaces for semi-structured document discovery and selection

With the increase in electronic publications, and indeed the availability of existing publications in digital form, as well as the encouragement of open access publication, comes a challenge. That challenge is to create assistive software to aid in the discovery and selection of relevant documents to one’s information need. Visual interfaces have begun to address the need of information seekers in finding publications and wading through the large result sets that are returned from search engines. There is currently, little evidence to suggest that these interfaces are based on systematic research on requirements. In this article, we examine areas which contribute to the theory and practice of visual interfaces directly relating to the discovery and selection of publications. We bring together work from different fields in a targeted approach to assist the future creation of these interfaces

Integrating Statistics and Visualization to Improve Exploratory Social Network Analysis

Social network analysis is emerging as a key technique to understanding social, cultural and economic phenomena. However, social network analysis is inherently complex since analysts must understand every individual's attributes as well as relationships between individuals. There are many statistical algorithms which reveal nodes that occupy key social positions and form cohesive social groups. However, it is difficult to find outliers and patterns in strictly quantitative output. In these situations, information visualizations can enable users to make sense of their data, but typical network visualizations are often hard to interpret because of overlapping nodes and tangled edges. My first contribution improves the process of exploratory social network analysis. I have designed and implemented a novel social network analysis tool, SocialAction (http://www.cs.umd.edu/hcil/socialaction) , that integrates both statistics and visualizations to enable users to quickly derive the benefits of both. Statistics are used to detect important individuals, relationships, and clusters. Instead of tabular display of numbers, the results are integrated with a network visualization in which users can easily and dynamically filter nodes and edges. The visualizations simplify the statistical results, facilitating sensemaking and discovery of features such as distributions, patterns, trends, gaps and outliers. The statistics simplify the comprehension of a sometimes chaotic visualization, allowing users to focus on statistically significant nodes and edges. SocialAction was also designed to help analysts explore non-social networks, such as citation, communication, financial and biological networks. My second contribution extends lessons learned from SocialAction and provides designs guidelines for interactive techniques to improve exploratory data analysis. A taxonomy of seven interactive techniques are augmented with computed attributes from statistics and data mining to improve information visualization exploration. Furthermore, systematic yet flexible design goals are provided to help guide domain experts through complex analysis over days, weeks and months. My third contribution demonstrates the effectiveness of long term case studies with domain experts to measure creative activities of information visualization users. Evaluating information visualization tools is problematic because controlled studies may not effectively represent the workflow of analysts. Discoveries occur over weeks and months, and exploratory tasks may be poorly defined. To capture authentic insights, I designed an evaluation methodology that used structured and replicated long-term case studies. The methodology was implemented on unique domain experts that demonstrated the effectiveness of integrating statistics and visualization

Practical and Rich User Digitization

A long-standing vision in computer science has been to evolve computing devices into proactive assistants that enhance our productivity, health and wellness, and many other facets of our lives. User digitization is crucial in achieving this vision as it allows computers to intimately understand their users, capturing activity, pose, routine, and behavior. Today's consumer devices - like smartphones and smartwatches provide a glimpse of this potential, offering coarse digital representations of users with metrics such as step count, heart rate, and a handful of human activities like running and biking. Even these very low-dimensional representations are already bringing value to millions of people's lives, but there is significant potential for improvement. On the other end, professional, high-fidelity comprehensive user digitization systems exist. For example, motion capture suits and multi-camera rigs that digitize our full body and appearance, and scanning machines such as MRI capture our detailed anatomy. However, these carry significant user practicality burdens, such as financial, privacy, ergonomic, aesthetic, and instrumentation considerations, that preclude consumer use. In general, the higher the fidelity of capture, the lower the user's practicality. Most conventional approaches strike a balance between user practicality and digitization fidelity. My research aims to break this trend, developing sensing systems that increase user digitization fidelity to create new and powerful computing experiences while retaining or even improving user practicality and accessibility, allowing such technologies to have a societal impact. Armed with such knowledge, our future devices could offer longitudinal health tracking, more productive work environments, full body avatars in extended reality, and embodied telepresence experiences, to name just a few domains.Comment: PhD thesi

Grounding for a computational model of place

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Text printed 2 columns per page.Includes bibliographical references (leaves 66-70).Places are spatial locations that have been given meaning by human experience. The sense of a place is it's support for experiences and the emotional responses associated with them. This sense provides direction and focus for our daily lives. Physical maps and their electronic decedents deconstruct places into discrete data and require user interpretation to reconstruct the original sense of place. Is it possible to create maps that preserve this sense of place and successfully communicate it to the user? This thesis presents a model, and an application upon that model, that captures sense of place for translation, rather then requires the user to recreate it from disparate data. By grounding a human place-sense for machine interpretation, new presentations of space can be presented that more accurately mirror human cognitive conceptions. By using measures of semantic distance a user can observe the proximity of place not only in distance but also by context or association. Applications built upon this model can then construct representations that show places that are similar in feeling or reasonable destinations given the user's current location.(cont.) To accomplish this, the model attempts to understand place in the context a human might by using commonsense reasoning to analyze textual descriptions of place, and implicit statements of support for the role of these places in natural activity. It produces a semantic description of a place in terms of human action and emotion. Representations built upon these descriptions can offer powerful changes in the cognitive processing of space.Matthew Curtis Hockenberry.S.M

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions

Understanding and conceptualising the document triage process through information seekers' visual and navigational attention

Information, is a valuable commodity and its effective use is a vital part of everyday life. With the advancements of the internet and the increasing accessibility to it, the location of information is no longer the primary concern of information seekers. Digitisation techniques have made a wide variety of documents available on-line, and more and more publications are being published in electronic form simultaneously to their physical counterpart. The largest challenge currently facing information seekers is that of locating the correct information from the abundance available to them. Whenever a search query is made, the user is inundated with multiple options of documents to choose from. These documents are all deemed to have some relevance to the query produced by using an information retrieval algorithm. Thus far, automatic support has only been provided until the document retrieval level. The user is then left to search through the result set, mostly unaided, by the system he is using. In order to facilitate support for the users, a solid understanding of the information seeker's behaviours during this triage process is vital. Thus far, research into the behaviour of information seekers during the specific triage behaviour is limited. Even more limited however, is the evidence reporting the visual attention of the users. Since the triage process is highly visual, this important element need to be thoroughly evidence before accurately conceptualising the entire process. For this reason, this thesis aims to investigate the visual attention of information seekers during the document triage process. This will inform the modelling and conceptualisation of information seekers' behaviour during triage. In turn, this can be used to inform the design of supportive software. The thesis contains a review of related research and identifies the gaps that needs further investigation. From these, a series of user studies are then conducted on document triage. These in turn, facilitate the formulation and discussion of 2 document triage models and measurements to record the effectiveness of document triage. We study the visual attention of information seekers in four lab based studies, eliciting their exact gaze and focus details. We expand current research in the information seeking domain by reporting on findings from users' triage activities on small screen devices and when under time constraints. Furthermore, a high level diary study, gives us richer data on participants' triage activities over a larger period of time in their natural surroundings. All the studies are brought together to elicit requirements and measurements to understand system and user efficiency during each stage of the triage process

Designing navigable information spaces

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 159-164).by Mark A. Foltz.M.S